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      DNA methylation analysis on purified neurons and glia dissects age and Alzheimer’s disease-specific changes in the human cortex

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          Abstract

          Background

          Epigenome-wide association studies (EWAS) based on human brain samples allow a deep and direct understanding of epigenetic dysregulation in Alzheimer’s disease (AD). However, strong variation of cell-type proportions across brain tissue samples represents a significant source of data noise. Here, we report the first EWAS based on sorted neuronal and non-neuronal (mostly glia) nuclei from postmortem human brain tissues.

          Results

          We show that cell sorting strongly enhances the robust detection of disease-related DNA methylation changes even in a relatively small cohort. We identify numerous genes with cell-type-specific methylation signatures and document differential methylation dynamics associated with aging specifically in neurons such as CLU, SYNJ2 and NCOR2 or in glia RAI1, CXXC5 and INPP5A. Further, we found neuron or glia-specific associations with AD Braak stage progression at genes such as MCF2L, ANK1, MAP2, LRRC8B, STK32C and S100B. A comparison of our study with previous tissue-based EWAS validates multiple AD-associated DNA methylation signals and additionally specifies their origin to neuron, e.g., HOXA3 or glia ( ANK1). In a meta-analysis, we reveal two novel previously unrecognized methylation changes at the key AD risk genes APP and ADAM17.

          Conclusions

          Our data highlight the complex interplay between disease, age and cell-type-specific methylation changes in AD risk genes thus offering new perspectives for the validation and interpretation of large EWAS results.

          Electronic supplementary material

          The online version of this article (10.1186/s13072-018-0211-3) contains supplementary material, which is available to authorized users.

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          Most cited references146

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Robust enumeration of cell subsets from tissue expression profiles

            We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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              Adjusting batch effects in microarray expression data using empirical Bayes methods.

              Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
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                Author and article information

                Contributors
                gillesgasparoni@gmail.com
                bultmann@biologie.uni-muenchen.de
                p.lutsik@dkfz-heidelberg.de
                Theo.Kraus@med.uni-muenchen.de
                Sabrina.Pichler@uks.eu
                Julia.Vlcek@med.uni-muenchen.de
                Vanessa.Dietinger@med.uni-muenchen.de
                Martina.Steinmaurer@med.uni-muenchen.de
                Melanie.Haider@med.uni-muenchen.de
                mulholland@biologie.uni-muenchen.de
                Thomas.Arzberger@med.uni-muenchen.de
                Sigrun.Roeber@med.uni-muenchen.de
                Matthias.Riemenschneider@uks.eu
                Hans.Kretzschmar@med.uni-muenchen.de
                Armin.Giese@med.uni-muenchen.de
                H.Leonhardt@lmu.de
                j.walter@mx.uni-saarland.de
                Journal
                Epigenetics Chromatin
                Epigenetics Chromatin
                Epigenetics & Chromatin
                BioMed Central (London )
                1756-8935
                25 July 2018
                25 July 2018
                2018
                : 11
                : 41
                Affiliations
                [1 ]ISNI 0000 0001 2167 7588, GRID grid.11749.3a, Department of Genetics, , University of Saarland (UdS), ; Campus, 66123 Saarbrücken, Germany
                [2 ]ISNI 0000 0004 1936 973X, GRID grid.5252.0, Department of Biology and Center for Integrated Protein Science, , Ludwig-Maximilians-University (LMU), ; 82152 Munich, Germany
                [3 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, Epigenomics and Cancer Risk Factors, , German Cancer Research Center (DKFZ), ; 69120 Heidelberg, Germany
                [4 ]ISNI 0000 0004 1936 973X, GRID grid.5252.0, Center for Neuropathology and Prion Research, , Ludwig-Maximilians-University (LMU), ; 82152 Munich, Germany
                [5 ]GRID grid.411937.9, Department of Psychiatry and Psychotherapy, , Saarland University Hospital (UKS), ; 66424 Homburg, Germany
                Author information
                http://orcid.org/0000-0002-6423-4637
                Article
                211
                10.1186/s13072-018-0211-3
                6058387
                30045751
                b9daf2c9-2245-4ddf-b53c-9ceafd3a5909
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 30 April 2018
                : 17 July 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
                Award ID: 01KU1216F
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: SFB 1064 A17/22
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Genetics
                dna methylation,epigenetics,alzheimer’s disease,neurodegeneration,aging,cell sorting,neuron,glia,brain,ewas
                Genetics
                dna methylation, epigenetics, alzheimer’s disease, neurodegeneration, aging, cell sorting, neuron, glia, brain, ewas

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